Ride-hailing services have grown in cities around the world. There are, however, few studies and even fewer publicly available data sources that provide a basis to understand and quantify changes in ride-hailing usage over time. Ride-hailing use may change over time because of socio-demographic shifts, economic and technological changes, and service attribute enhancements, as well as changes in unobserved attributes such as attitudes and perceptions, lifestyle preferences, technology savviness, and social influences. It is important to quantify the effects of these different forces on ride-hailing frequency so that robust forecasts of ride-hailing use can be developed. This paper uses repeated cross-sectional data collected in 2015 and 2017 in the Puget Sound region to analyze the differential effects of socio-demographic variables on the evolution of ride-hailing adoption and usage. By doing so, the study is able to isolate and quantify the pure effect of the passage of time on adoption of ride-hailing services. A joint binary probit-ordered probit model is estimated on the pooled dataset to explicitly account for sample-selection differences between the 2015 and 2017 surveys that may affect estimates of ride-hailing adoption in the two years. Model estimation results are used to compute average treatment effects of different variables on ride-hailing usage over time. It is found that the effects of most demographic variables on individuals’ propensity to use ride-hailing are softening over time, leading to reduced differences in ride-hailing use among market segments. This suggests that there is a “democratization” of ride-hailing services over time.

乘车服务在世界各地的城市中都在增长。但是，很少有研究，甚至没有更多的公共可用数据源，它们为理解和量化乘车使用量随时间的变化提供了基础。由于社会人口结构的变化，经济和技术的变化以及服务属性的提高，以及态度和观念，生活方式，技术偏好和社会影响力等未观察到的属性的变化，乘车习惯的使用可能会随着时间而改变。重要的是量化这些不同的力量对乘车频率的影响，以便可以开发出可靠的乘车使用预测。本文使用普吉特海湾地区2015年和2017年收集的重复横截面数据来分析社会人口统计学变量对乘车服务采用和使用演变的不同影响。通过这样做，该研究能够隔离和量化时间流逝对乘车服务采用的纯粹影响。在合并的数据集上估算了一个联合的二进制按概率排序的概率模型，以明确考虑2015年和2017年调查之间的样本选择差异，这可能会影响这两年中对乘车服务采用的估计。模型估计结果用于计算不同变量对乘车习惯的平均治疗效果。发现大多数人口变量对个人使用网约车的倾向的影响随着时间的推移逐渐减弱，从而减少了细分市场之间的乘车使用差异。这表明，随着时间的推移，打车服务出现了“民主化”。